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Principal Investigator
James Morrison
University of Maryland
Position Title
Informatics Fellow
About this CDAS Project
NLST (Learn more about this study)
Project ID
Initial CDAS Request Approval
Sep 12, 2013
Personalized Medicine: A New Paradigm of Decision Support for Pulmonary Nodules Using the National Lung Screening Trial Dataset
Standard guidelines such as the Fleischner Society criteria are commonly used to recommend further diagnostic follow-up of pulmonary nodules. These guidelines rely on patient risk factors with stratified recommendations determined by nodule size. While these criteria have evolved to include a few additional diagnostic characteristics than just nodule size, recommendations are still made by assigning patients into large generic categories. As the understanding of the natural evolution of lung nodules expands, the diagnostic recommendations should become individualized. The national lung cancer screening trial (NLST) dataset provides an unparalleled resource for matching patients with newly discovered pulmonary nodules and existing patient data with similar nodule findings and demographics. These matched data can be used to inform a more sophisticated personalized diagnostic decision-making process by tailoring imaging follow-up intervals and possibly guiding intervention or prognosis.

1. Match demographic information and nodule characteristics of patients with newly discovered pulmonary nodules to similar patients within the NLST dataset.
2. Provide real-time diagnostic decision support for future imaging, intervention, and prognosis based upon the matched NLST cohort.
3. Use our findings as a model for matching based on evolving clinical databases.


Jason Hostetter, University of Maryland
Ken Wang, Baltimore VA Medical Center
Eliot Siegel, University of Maryland - Baltimore VA Medical Center

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